Er et al. BMC Structural Biology 2011, 11:43
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RESEARCH ARTICLE
Open Access
Computational analysis of a novel mutation in
ETFDH gene highlights its long-range effects on
the FAD-binding motif
Tze-Kiong Er1,2†, Chih-Chieh Chen3†, Yen-Yi Liu3, Hui-Chiu Chang2, Yin-Hsiu Chien4, Jan-Gowth Chang1,6,7,
Jenn-Kang Hwang3* and Yuh-Jyh Jong1,2,5*
Abstract
Background: Multiple acyl-coenzyme A dehydrogenase deficiency (MADD) is an autosomal recessive disease
caused by the defects in the mitochondrial electron transfer system and the metabolism of fatty acids. Recently,
mutations in electron transfer flavoprotein dehydrogenase (ETFDH) gene, encoding electron transfer flavoprotein:
ubiquinone oxidoreductase (ETF:QO) have been reported to be the major causes of riboflavin-responsive MADD. To
date, no studies have been performed to explore the functional impact of these mutations or their mechanism of
disrupting enzyme activity.
Results: High resolution melting (HRM) analysis and sequencing of the entire ETFDH gene revealed a novel
mutation (p.Phe128Ser) and the hotspot mutation (p.Ala84Thr) from a patient with MADD. According to the
predicted 3D structure of ETF:QO, the two mutations are located within the flavin adenine dinucleotide (FAD)
binding domain; however, the two residues do not have direct interactions with the FAD ligand. Using molecular
dynamics (MD) simulations and normal mode analysis (NMA), we found that the p.Ala84Thr and p.Phe128Ser
mutations are most likely to alter the protein structure near the FAD binding site as well as disrupt the stability of
the FAD binding required for the activation of ETF:QO. Intriguingly, NMA revealed that several reported diseasecausing mutations in the ETF:QO protein show highly correlated motions with the FAD-binding site.
Conclusions: Based on the present findings, we conclude that the changes made to the amino acids in ETF:QO
are likely to influence the FAD-binding stability.
Background
Multiple acyl-CoA dehydrogenase deficiency (MADD),
also known as glutaric aciduria type II, is an autosomal
recessively inherited disorder of fatty acid metabolism [1].
In the majority of cases, the disorder is caused by a defect
in either the alpha- or beta-subunit of the electron transfer
flavoprotein (ETFA; OMIM 608053, ETFB; OMIM
130410) or the electron transfer flavoprotein dehydrogenase (ETFDH; OMIM 231675) genes. However, in some
patients, the disorder may result from a few, currently
* Correspondence: [email protected]; [email protected]
† Contributed equally
1
Division of Molecular Diagnostics, Department of Laboratory Medicine,
Kaohsiung Medical University Hospital, Kaohsiung Medical University, 100
Shih-Chuan 1st Rd., Kaohsiung, 80708, Taiwan
3
Institute of Bioinformatics and Systems Biology, National Chiao Tung
University, 75 Bo-Ai Street, Hsinchu, 30068, Taiwan
Full list of author information is available at the end of the article
unidentified, disruptions to riboflavin metabolism [2-4].
Because of a deficiency of ETF or ETFDH, the acyl-CoA
dehydrogenases are unable to transfer the electrons generated by the dehydrogenation reactions. This results in the
accumulation of various intramitochondrial acyl-CoA
esters. Recently, ETFDH mutations were reported to be
major causes of riboflavin-responsive MADD [5].
Characteristics such as organic aciduria may be detected
only during periods of illness or catabolic stress. In the
majority of cases, depending on the metabolic status of the
patient, plasma acylcarnitine analysis may not provide a
definitive diagnosis. Amongst the most daunting challenges facing MADD is metabolic heterogeneity, in particular, for the diagnosis of MADD. Thus, numerous
molecular diagnostic methods have been established for
analyzing the ETF and/or ETFDH genes for the purpose of
achieving the final diagnosis of MADD. Previously, we
© 2011 Er et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
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reconfirmed the high prevalence of the c.250G > A (p.
Ala84Thr) mutation in Taiwanese patients with riboflavinresponsive MADD, as well as in the normal population
using a high-resolution melting (HRM) analysis [6]. It
should be noted that all the nine patients examined,
including three pairs of siblings, harbor this mutation and
four were homozygous. We estimated the allele frequency
to be 0.004 for the p.Ala84Thr mutant (c.250G > A).
Thus, the estimated carrier frequency of this mutation in
the Taiwanese population is 1:125 (0.8%). The homozygous mutant frequency was 1:62,500. Nevertheless, the
carrier frequency of c.250G > A is estimated to be 1.35%
amongst the population in southern China. Thus,
~1:22,000 Han Chinese are expected to suffer from riboflavin-responsive MADD [7]. Intriguingly, p.Ala84Thr has
been identified in Asian countries including Taiwan, Hong
Kong, Singapore, Thailand and southern China [6-11].
A total of 69 patients were examined from the above referenced literature and diagnosed with late-onset MADD.
Amongst these patients, the mutation c.250G > A in the
ETFDH gene was detected in 49 patients with homozygous mutations and 20 patients with heterozygous mutations. To date, this mutation has never been reported in
Western countries [5]. Thus, it has been suggested that
the most common mutation p.Ala84Thr is ethnic-specific.
In addition, the mutation of p.Ala84Thr most likely
accounts for the mild phenotype and response to riboflavin. Here, a patient presented at 11/2 years represented a
mild to late-onset MADD and the patient slightly
improved following riboflavin treatment (Additional file 1,
Table S1). The patient had compound heterozygous mutations (p.Ala84Thr/p.Phe128Ser) of the ETFDH gene. Currently, there is no related study to explore the underlying
mechanism for the most common mutation (p.Ala84Thr).
Flavin adenine dinucleotide (FAD) is a cofactor for the
electron transfer flavoprotein:ubiquinone oxidoreductase
(ETF:QO; encoded by the ETFDH gene) which constitutes the electron-transport pathway for a number of
mitochondrial flavoprotein dehydrogenases that are
mainly involved in fatty-acid and amino-acid metabolism. Riboflavin (the precursor of FAD) treatment has
been shown to strikingly ameliorate the symptoms and
metabolic profiles in MADD patients [1,4,6,12]. This
may enhance the conformational stabilization of the
mutant ETF:QO protein [13]. Two of the mutations (p.
Ala84Thr and p.Phr128Ser) are located in the FADbinding domain; however, the two amino acids do not
have direct interactions with FAD according to the predicted 3D structure of ETF:QO. The closest distances
between the mutations (p.Ala84Thr and p.Phr128Ser)
and the FAD binding residues are 8.6 and 13.6 Å,
respectively. Therefore, to explore the effects of the
mutations on ETF:QO dynamics, molecular dynamics
(MD) simulations of the wild type (WT) and mutant
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type (MT) ETF:QO in the same model environment
were compared. Besides the MD simulations, an alternative method, normal mode analysis (NMA), for studying
protein motions was used to analyze the dynamic correlations between the mutation sites and the FAD-binding
motif.
In this study, molecular modeling, MD simulations
and NMA were performed to investigate the structural
implications of the underlying mutations on the protein
conformation. The obtained results may provide an
insight into the pathomechanism of MADD.
Methods
Patients
The patient was a girl aged 11/2 years who was clinically
diagnosed with MADD in 2009. Her dried-blood spot
sample was sent for acylcarnitine analysis. The results
demonstrated elevation of acylcarnitines during the illness,
consistent with glutaric aciduria type 2. Gas chromatography-mass spectrometry revealed significant elevations of
urinary glutaric acid, 3-methylglutarate, 3-methylcluconate, adipate, 3-methyladipate, octenedioate, suberate, and
hexanoylglycine. The present study was approved by the
Institutional Review Board (IRB) of Kaohsiung Medical
University Hospital.
Genetic analysis
The blood samples were collected from the patient and
the family members as well as from normal controls
with informed consents. The genomic DNA samples
were extracted from peripheral whole blood using the
NucleoSpin® Blood Kit (Macherey-Nagel), according to
the manufacturer’s instructions.
Identification of mutations
HRM analysis was applied for screening the mutation of
ETFDH, as previously described [6]. PCR was carried out
in duplicate in a 10 μL final volume using the LightCycler
®
480 High-resolution Melting Master (Reference
04909631001, Roche Diagnostics) 1 × buffer which contained Taq polymerase, nucleotides, the dye ResoLight
and 30 ng DNA. The primers and MgCl2 were used at a
concentration of 0.25 μM and 2.5 mM, respectively for
identifying the ETFDH gene mutations.
The HRM assays were conducted using the LightCycler® 480 instrument (Roche Diagnostics) provided with
the software LightCycler® 480 Gene-Scanning Software
Version 1.5 (Roche Diagnostics).
The PCR program required a SYBR Green I filter (533
nm). The program consisted of an initial denaturationactivation step at 95°C for 10 min, followed by a 45-cycle
program. The 45-cycle program consisted of the following
steps: denaturation at 95°C for 15 s, annealing at 58°C or
60°C for 15 s and elongation at 72°C for 15 s with the
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reading of the fluorescence. The acquisition mode was single. The melting program consisted of three steps: denaturation at 95°C for 1 min, renaturation at 40°C for 1 min,
and the subsequent melting that included continuous
fluorescent readings from 60 to 90°C at a rate of 25 acquisitions per degree centigrade.
To confirm the results of the HRM analysis, a sequencing analysis was also performed for all the samples. After
the HRM analysis, the samples were purified using the
FavorPrep™ PCR clean-up mini kit. The PCR products
generated after HRM were sequenced directly. The
sequence reaction was performed in a final volume of
10 μL, comprising 5 μL of the purified PCR product,
2.5 μM of each PCR primer and 1 μL of the ABI PRISM
terminator cycle sequencing kit v1.1 (Applied Biosystems).
The sequencing program started from 96°C for 1 min and
then followed by 25-cycle PCR program. The program
consisted of the following steps: denaturation at 96°C for
10 s; annealing at 50°C for 5 s; and elongation at 60°C for
4 min. The sequence detection was performed using the
ABI Prism 3130 Genetic Analyzer (Applied Biosystems).
To ensure that the mutation found in the patient was the
one specifically present in patients with MADD, 60 healthy
control chromosomes were also screened.
Sorting intolerant from tolerant (SIFT) analysis
SIFT tool [14-16] generates multiple alignments of the
sequences over different species to look at the conserved
sequence of a gene; it asses the conserved amino acid positions and analyzes the effect of missense changes on the
conserved structure of proteins over the course of evolution. The SIFT tool assigns a score to the mutations, and
the score of < 0.05 is considered potentially damaging.
Structure prediction of human wild type (WT) and mutant
type (MT) ETF:QO
Three-dimensional (3D) structures of the human ETF:QO
were predicted using the (PS)2 server [17,18]. (PS)2 automatically uses an effective consensus strategy. The above
mentioned strategy combines structural- [18] and profilebased [19-21] comparison methods for both template
selection and target-template alignment. (PS) 2 server
selected the porcine ETF:QO (PDB entry: 2GMH) [22] as
the model template. The ETF:QO models were built based
on a sequence and structural alignment from the above
mentioned template. A total of six models were yielded for
each target by using a set of parameters. These models
were based on the alternative target-template alignments.
The qualities of the modeled structures were checked
using Ramachandran plots [23]. The model with the highest quality was selected as the final predicted structure for
the target. Finally, the protein-ligand complex was constructed by superimposing the predicted human ETF:QO
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model to the 3D structure of the porcine ETF:QO-ligand
complex.
Molecular dynamics simulations
Molecular dynamics (MD) simulations were performed
using the software GROMACS ver. 4.5.1 [24]. OPLS-AA
all-atom force field was used for the energy calculations.
The models were solvated with simple point charge
(SPC) water molecules, and the models were simulated
in a triclinic box [25] with periodic boundary conditions.
The simulations were performed in the canonical NVT
(number of particles, volume and temperature) ensemble. The energy of the models was first minimized using
the steepest descent algorithm. This was followed by the
position-restrained MD simulations for 200 ps. The linear constraint solver (LINCS) algorithm was used to
constrain the bonds [26]. The MD simulations were carried out at constant pressure and temperature for 2.0 ns
using an integration time step of 2 fs. The non-bonded
interactions were cutoff at 10 Å. At every 100 time
steps, the coordinates from the MD simulations were
saved.
Analysis of MD trajectories
The objective was to understand the structural and functional implications of the amino acid substitution. Thus,
the trajectories of WT and MT were analyzed for the following structural properties as a function of time: a) the
root mean square deviation (RMSD) of the Ca atoms with
respect to the starting conformation; b) RMSD of the
FAD-binding motif with respect to the starting conformation; and c) the B-factors [27] of the Ca atoms which were
calculated from the last 1 ns of the MD trajectories. The
B-factors in protein structures reflect the fluctuation of
atoms about their average positions. A large B-factor indicates high flexibility of individual atoms.
Normal mode analysis
Normal mode analysis (NMA) is a powerful tool to estimate the dynamics based on structure [28-31]. Low-frequency normal modes describing the large-scale realworld motions of a protein have been demonstrated to
be related to biological function [28,32]. In the present
study, the NMA was performed using the software GROMACS ver. 4.5.1 by diagonalization of the mass-weighted
Hessian matrix. The force field-based NMA requires
careful energy minimization of the initial structure. We
first minimized the human ETF:QO structure with the
steepest descent integrator for a couple of steps. Next,
the L-BFGS integrator was used to minimize the maximum force close to zero and subsequently generate a
Hessian matrix. The NMA was performed using the algorithm in GROMACS. Finally, the pair-wise residue
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correlation was calculated. The above mentioned correlation was calculated as the average correlation summed
over 50 lowest nonzero frequency normal modes [33].
Results
Patient and molecular diagnostic
HRM analysis of ETFDH gene revealed a novel T > C
point mutation at position 383. The point mutations
were confirmed by direct DNA sequencing (Figures 1A1D). The hotspot mutation c.250G > A (p.Ala84Thr)
(Figures 1A and 1B) and a novel mutation c.383T > C (p.
Phe128Ser) (Figures 1C and 1D) in the ETFDH gene
were identified in this patient. These mutations are highly
conserved amongst the different species (Additional
file 2, Table S2). The SIFT tool analysis revealed a score
of < 0.05 and predicted that the replaced amino-acid was
potentially damaging. This indicates that these mutations
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are most likely pathogenic. Figures 1E and 1F show the
melting profiles of the ETFDH mutations in the patient’s
family. The patient had compound heterozygous mutations (p.Ala84Thr/p.Phe128Ser). The sibling was a carrier
of c.383T > C (p.Phe128Ser). The father and mother
were carriers of c.383T > C (p.Phe128Ser) in exon 3 and
c.250G > A (p.Ala84Thr) in exon 3, respectively (Figures
1E and 1F). The above mentioned mutation was
not detected in the 60 unrelated control chromosomes
(wild type) (Figure 1G). The pedigree is presented in
Figure 1H.
Structure prediction of human ETF:QO
Human ETF:QO models were constructed using the
published porcine ETF:QO crystal structure (PDB Id:
2GMH) as the template (Figure 2A). A close-up view of
the hydrophobic residues which are located at the FAD-
Figure 1 HRM analysis of ETFDH gene. Electropherograms of various mutations: (A) c.250G > A (wild type); (B) c.250G > A (heterozygous); (C)
c.383T > C (wild type); (D) c.383T > C (heterozygous); (E) Normalized and temperature-shifted difference plots, the melting profile of c.250G > A;
(F) Normalized and temperature-shifted difference plots, the melting profile of c.383T > C; (G) The mutation of c.383T > C was not detected in
60 unrelated control chromosomes (wild type); (H) Affected pedigree with familial segregation of both mutations. Squares, male subjects; circles,
female subjects. Affected and unaffected subjects are represented by solid and open symbols, respectively. The subject with a sample available
for the present study is indicated with an asterisk.
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Figure 2 Structure prediction of ETF:QO. (A) The predicted wild-type model of human ETF:QO. The structure comprises three domains: the
FAD-binding domain (gray), the ubiquinone-binding domain (blue) and the 4Fe4S cluster domain (red). The three redox centers: FAD,
ubiquinone and the 4Fe4S cluster are shown in ball-and-stick representations. A cluster of hydrophobic residues which are located at the FADbinding domain and at a distance below 4 Å around the mutation residues (p.Ala84Thr and p.Phe128Ser) are depicted in the sphere model. (B)
A close-up view of the hydrophobic residues (V71, I73, A84, V85, L87, V100, L127 and F128). The a-helices and b-strands are also labeled. (C)
Model of the p.Ala84Thr mutant in which the hydrophobic residue A84 is replaced by the hydrophilic residue threonine (yellow). (D) Model of
the Phe128Ser mutant in which the hydrophobic residue F128 is replaced by the hydrophilic residue serine (yellow). The 3D molecular graphs
are displayed using PyMOL [51].
binding domain, including V71, I73, A84, V85, L87,
V100, L127, and F128 is shown in Figure 2B. The FADbinding residues are located at the loop that connects
strand b1 and helix a1. Therefore, it is reasonable to
hypothesize that these hydrophobic residues would most
likely participate in hydrophobic interactions to stabilize
FAD binding. The ETF:QO mutations of p.Ala84Thr
and p.Phe128Ser were constructed in the same way as
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the WT structure. Both replacements are located at the
center and peripheral of the hydrophobic patch, respectively (Figures 2C and 2D).
Molecular dynamics simulations
For the MD simulations, the trajectories of the WT and
MT ETF:QO in the explicit solvent were calculated. The
backbone RMSD values for WT and MT ETF:QO during the production phase relative to the starting structures were plotted (Additional file 3, Figure S1). These
values were plotted to obtain an estimate of the MD trajectory quality and convergence. For the simulations of
WT and MT ETF:QO, the obtained data demonstrate
that after a rapid increase during the first 0.2 ns, the trajectories are stable with average values of 1.43, 1.38 and
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1.51 Å for WT and the p.Ala84Thr and p.Phe128Ser
ETF:QO mutants, respectively. Statistical analysis of the
RMSD data reveals that the trajectories are more stable
after the first 1.0 ns. Therefore, only the second half of
the trajectory was analyzed.
Based on Zhang’s study [22], a segment corresponding
to residues 71-90 of human ETF:QO was found to form
the FAD-binding motif. The binding site was located at
the b1/a1 loop (Figure 2B). The RMSD of the FADbinding motif with respect to the starting conformation
was compared for the WT and MT structures during
the course of the simulations. The RMSD was found to
increase as a function of time for the MT p.Ala84Thr
(Figure 3A) and MT p.Phe128Ser (Figure 3B) when
compared with the WT.
Figure 3 RMSD plots of the FAD-binding motif. Comparison of the RMSD plots of the FAD-binding motif (residues 71-90) of WT and MT (A)
p.Ala84Thr and (B) p.Phe128Ser structures with respect to the starting conformation during the course of the simulation. WT and MT plots are
presented in blue and red, respectively.
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The analysis of the B-factors for each residue at the b1,
a1, a2, and a3 regions revealed that the atomic fluctuations of the p.Ala84Thr and p.Phe128Ser mutants were
significant at the b1/a1 loop (involved in the FAD binding) when compared with the WT values (Figure 4). Helix
a2, which may be involved in the support and stabilization
of the a1 and a3 helices, was also found to have higher Bfactors in both mutants (Figures 4B and 4C). Moreover,
the fluctuations of the MT p.Phe128Ser were also
observed to be significant at helix a3 (Figure 4C). From
the above mentioned analysis, it was observed that the
mutations of p.Ala84Thr and p.Phe128Ser may induce
additional fluctuations in the b1/a1 loop, a2, and a3
regions. Such fluctuations may promote instability in this
region of the protein and hamper FAD binding.
Normal mode analysis
The correlation map for ETF:QO is shown in Figure 5.
General structural elements of ETF:QO can be identified
by the pattern of the cross-correlations. The enlargements along the diagonal are typical for a-helices, where
motions of amino acids adjacent in the sequence are correlated, such as the elements of the helices a1, a2 and a3
(Figure 5). Cross-correlations that extend as plumes from
the diagonal correspond to anti-parallel elements of the
secondary structure such as the strands b5, b6, b7 and b8
(Figure 5). The cross-correlations are also found between
the multiple structural elements, whereby some result
from specific contacts, while others are longer range in
nature. For example, long-range cross-correlations are
observed between the FAD-binding motif (colored in
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blue) and helix a2 (black square 1). Between the FADbinding motif and helix a2, correlations exist because of
the presence of a hydrophobic patch consisting of V85
(helix a1), L127 (helix a2) and F128 (helix a2) (Figure
2B). Helix a3 also displays significant correlated motion
with the FAD-binding motif and helix a2 (black squares
2 and 3). These results suggest correlated dynamics
between the FAD-binding motif, helix a2 and helix a3.
Discussion
In this study, we found that the p.Ala84Thr and p.
Phe128Ser mutations are most likely to alter the protein
structure near the FAD binding site as well as disrupt
the stability of the FAD binding required for the activation of ETF:QO using MD simulations and NMA. Interestingly, NMA revealed that several reported diseasecausing mutations in the ETF:QO protein show highly
correlated motions with the FAD-binding site.
In the FAD-binding family, the pyrophosphate moiety
binds to the most strongly conserved sequence motif
[22]. A subset of the proteins that adopt the Rossmann
fold [34-36] also bind to the nucleotide cofactors such
as FAD and NAD(P), and function as oxidoreductases.
The Rossmann fold can often be identified by the short
amino-acid sequence motif, GxGxxG. The pivotal role
of the Gly residues in the conserved central GxGxxG is
well understood [37,38]. The Rossmann fold begins with
a b-strand connected by a short loop to an a-helix [39].
As previously described, the most conserved and wellstudied sequence motif, xhxhGxGxxGxxxhxxh(x)8hxhE
(D), is part of the Rossmann fold [37,40-42]. Here × is
Figure 4 B-factors analysis. Structures of the (A) WT, (B) p.Ala84Thr and (C) p.Phe128Ser ETF:QO proteins are drawn in cartoon putty
representation at the b1, a1, a2, and a3 regions, where the color is ramped by residue from blue as the lowest B-factor value to red as the
highest B-factor value. In addition, the size of the tube also reflects the value of the B-factor, where the larger the B-factor the thicker the tube.
The structures in the other regions are colored in white and displayed in cartoon tube representation, where the size of the tube is independent
of the B-factors. The red arrows indicate the positions of the mutations.
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Figure 5 Cross-correlation map calculated from the NMA. A negative value refers to an anti-correlation between residue fluctuations (blue),
whereas a positive value indicates concerted motion in the same direction (red).
any residue and h is a hydrophobic residue. The hydrophobic residues provide hydrophobic interactions
between the a-helix and the b-sheet. Intriguingly, the
mutation of p.Ala84Thr is located at a conserved hydrophobic residue of this motif of the Rossmann fold (Figure 2C). In addition, the mutation of p.Phe128Ser is also
located at this hydrophobic patch (Figure 2D). The
above mentioned observations, along with the obtained
results of the trajectory analyses (Figures 3 and 4),
strongly suggest that these mutations could influence
the structural stability of the FAD-binding motif. Thus,
it could lead to unstable FAD binding. Therefore, we
postulate that p.Ala84Thr and p.Phe128Ser may alter
the stability of the FAD-binding site and hamper efficient binding of the ligand. In turn, this instability of
the FAD-binding site may impair the activity of the electron-transport system.
The binding of FAD is important for the catalytic activity of flavoproteins as well as for the correct folding,
assembly and protein stability [43-45]. A reduced
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availability of intra-mitochondrial FAD may result in a less
stable or inactive conformation of the proteins. As a result,
an accelerated breakdown could result. However, supplementation of riboflavin is most likely to increase the intramitochondrial FAD concentration. As a result, FAD binding could be promoted. This could ameliorate the effect of
the mutations that reduce the affinity of ETF:QO for FAD
[5,13]. It should be noted that riboflavin treatment has
been shown to ameliorate the symptoms and metabolic
profiles in recent studies [5,6,8,11,12].
If the charge or size of the amino acid is altered and the
amino acid is buried within the core of the protein, folding
may not proceed and the result is a complete loss of function. Such a loss of function has been found for a large
number of variant short-chain acyl-CoA dehydrogenase
(SCAD) and medium-chain acyl-CoA dehydrogenase
(MCAD) proteins [46,47]. In addition, it may also cause
conformational changes to the 3D structure by altering its
hydrophobicity, charge, overpacking, or cavity at the buried site, leading to a temperate damage [48]. In the present
study, we identified a novel mutation, p.Phe128Ser. We
have used a NMA to characterize the detailed changes to
protein fluctuations. The results suggest that the dynamics
of the FAD-binding motif, helix a2 and helix a3 are correlated. The substitution of p.Phe128Ser may alter the
hydrophobic interactions between helix a1 and helix a2
(Figure 5). It should be noted that numerous mutations
near residue p.Phe128 have been identified. Wen et al.
[48] recently identified three ETFDH mutations, p.
Leu127Arg, p.Asp130Val, and p.Trp131Cys in riboflavinresponsive MADD patients in the north of China. Er et al.
[6] and Liang et al. [8] reported one ETFDH mutation, p.
Leu127His, in riboflavin-responsive MADD patients in the
Taiwanese population. Law et al. [11] also reported one
ETFDH mutation, p.Pro137Ser, in a Chinese family with
riboflavin-responsive MADD. In addition, Goodman et al.
[49] reported p.L138R as a disease-causing mutation.
Interestingly, these mutations are located very close to
each other in the region of the helix a2. Thus, these findings demonstrate the highly correlated motions of the
FAD-binding motif (Figure 5 and Additional file 4, Figure
S2). Previously recognized ETFDH mutations, p.
Arg175Leu and p.Arg175His [6-8,50], were found in the
region of helix a3. Both mutations have significantly correlated motions with the FAD-binding motif, as shown in
Figure 5. Based on the present findings, we believe that
the mutations to the amino acids in these regions are
likely to affect the FAD-binding stability. Therefore, it is
highly recommended that the ETFDH mutation in exon 3,
4 and 5 be initially screened. However, based on the B-factors analysis, the atomic fluctuations of the p.Leu127Arg,
and p.Arg175His were not significant at the b1/a1 loop,
which was involved in FAD binding (Additional file 5, Figure S3). According to published literatures, these MADD
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patients are compound heterozygous for two mutations.
Therefore, it appears probable that other mutations most
likely account for the disease state. Characterization of
additional mutations in the other region of ETF:QO will
be established in future studies. Currently, the present
study is the first to demonstrate through NMA that several previously reported ETFDH gene mutations have
highly correlated motions with the FAD-binding site.
Conclusions
The present study is the first demonstration of p.
Ala84Thr and p.Phe128Ser by molecular modeling, MD
simulations, and correlated motion analysis. Along with
the obtained results of the MD simulations and NMA,
the present findings strongly suggest that p.Ala84Thr
and p.Phe128Ser are likely to alter the ETF:QO protein
structure near the FAD-binding site as well as disrupt
the stability of FAD binding, which is essential for the
activation of ETF:QO. The change of hydrophobic interaction may destabilize FAD binding, giving rise to a less
stable or inactive conformation. Overall, the obtained
findings have implications for the pathogenic mechanism of riboflavin-responsive MADD.
Additional material
Additional file 1: Table S1. Dried blood spot acylcarnitine profile
measured using tandem mass spectrometry in the patient before and
after riboflavin treatment.
Additional file 2: Table S2. The highlighted amino acid alanine (A) and
phenylalanine (F) shown in yellow and pink, respectively, at positions 84
and 128 are conserved in all the orthologs.
Additional file 3: Figure S1. RMSD plots of the whole WT and MT (A) p.
A84T and (B) p.F128S structures with respect to the starting
conformation during the course of the MD simulations.
Additional file 4: Figure S2. Structure of ETF:QO with the positions of
the identified amino acid changes near residue F128 in riboflavinresponsive MADD patients labeled.
Additional file 5: Figure S3. Structures of the WT and MT ETF:QO are
drawn in cartoon putty representation. The red arrows indicate the
positions of the mutations.
Acknowledgements
The present study was supported by a grant from the Kaohsiung Medical
University Hospital (KMUH99-9M65) and National Science Council (NSC 992811-B-009-004-ASP). We are grateful to both the hardware and software
supports of the Structural Bioinformatics Core Facility at National Chiao Tung
University.
Author details
Division of Molecular Diagnostics, Department of Laboratory Medicine,
Kaohsiung Medical University Hospital, Kaohsiung Medical University, 100
Shih-Chuan 1st Rd., Kaohsiung, 80708, Taiwan. 2Graduate Institute of
Medicine, College of Medicine, Kaohsiung Medical University, 100 ShihChuan 1st Rd., Kaohsiung, 80708, Taiwan. 3Institute of Bioinformatics and
Systems Biology, National Chiao Tung University, 75 Bo-Ai Street, Hsinchu,
30068, Taiwan. 4Department of Medical Genetics and Pediatrics, National
Taiwan University Hospital, 8 Chung-Shan South Road, Taipei, 10041, Taiwan.
5
Department of Pediatrics, Kaohsiung Medical University Hospital, Kaohsiung
1
Er et al. BMC Structural Biology 2011, 11:43
http://www.biomedcentral.com/1472-6807/11/43
Medical University, 100 Shih-Chuan 1st Rd., Kaohsiung, 80708, Taiwan.
6
Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical
University, 100 Shih-Chuan 1st Rd., Kaohsiung, 80708, Taiwan. 7Center for
Excellence in Environmental Medicine, Kaohsiung Medical University, 100
Shih-Chuan 1st Rd., Kaohsiung, 80708, Taiwan.
Authors’ contributions
Concept and design of the experiments: TKE, CCC, YYL, HCC, JGC, JKH and
YJJ. Performance of the experiments: TKE and CCC. Data analysis and
discussion: TKE, CCC, YYL, HCC, YHC, JGC, JKH and YJJ. Contribution of
reagents/materials/analysis tools: HCC, YHC, JGC and JKH. Manuscript
preparation: TKE, CCC, JGC, JKH and YJJ. All authors read and approved the
final manuscript.
Page 10 of 11
14.
15.
16.
17.
18.
19.
Competing interests
The authors declare that they have no competing interests.
20.
Received: 27 June 2011 Accepted: 21 October 2011
Published: 21 October 2011
21.
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doi:10.1186/1472-6807-11-43
Cite this article as: Er et al.: Computational analysis of a novel mutation
in ETFDH gene highlights its long-range effects on the FAD-binding
motif. BMC Structural Biology 2011 11:43.
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